{"id":"https://openalex.org/W2090287317","doi":"https://doi.org/10.1145/1345448.1345451","title":"VISA","display_name":"VISA","publication_year":2007,"publication_date":"2007-12-01","ids":{"openalex":"https://openalex.org/W2090287317","doi":"https://doi.org/10.1145/1345448.1345451","mag":"2090287317"},"language":"en","primary_location":{"id":"doi:10.1145/1345448.1345451","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1345448.1345451","pdf_url":null,"source":{"id":"https://openalex.org/S4210176598","display_name":"ACM SIGKDD Explorations Newsletter","issn_l":"1931-0145","issn":["1931-0145","1931-0153"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM SIGKDD Explorations Newsletter","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5104360871","display_name":"Ira Assent","orcid":"https://orcid.org/0000-0002-1091-9948"},"institutions":[{"id":"https://openalex.org/I887968799","display_name":"RWTH Aachen University","ror":"https://ror.org/04xfq0f34","country_code":"DE","type":"education","lineage":["https://openalex.org/I887968799"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Ira Assent","raw_affiliation_strings":["RWTH Aachen University, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"RWTH Aachen University, Germany","institution_ids":["https://openalex.org/I887968799"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049176185","display_name":"Ralph Krieger","orcid":null},"institutions":[{"id":"https://openalex.org/I887968799","display_name":"RWTH Aachen University","ror":"https://ror.org/04xfq0f34","country_code":"DE","type":"education","lineage":["https://openalex.org/I887968799"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Ralph Krieger","raw_affiliation_strings":["RWTH Aachen University, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"RWTH Aachen University, Germany","institution_ids":["https://openalex.org/I887968799"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053906729","display_name":"Emmanuel M\u00fcller","orcid":"https://orcid.org/0000-0002-5409-6875"},"institutions":[{"id":"https://openalex.org/I887968799","display_name":"RWTH Aachen University","ror":"https://ror.org/04xfq0f34","country_code":"DE","type":"education","lineage":["https://openalex.org/I887968799"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Emmanuel M\u00fcller","raw_affiliation_strings":["RWTH Aachen University, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"RWTH Aachen University, Germany","institution_ids":["https://openalex.org/I887968799"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003335849","display_name":"Thomas Seidl","orcid":"https://orcid.org/0000-0002-4861-1412"},"institutions":[{"id":"https://openalex.org/I887968799","display_name":"RWTH Aachen University","ror":"https://ror.org/04xfq0f34","country_code":"DE","type":"education","lineage":["https://openalex.org/I887968799"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Thomas Seidl","raw_affiliation_strings":["RWTH Aachen University, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"RWTH Aachen University, Germany","institution_ids":["https://openalex.org/I887968799"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5104360871"],"corresponding_institution_ids":["https://openalex.org/I887968799"],"apc_list":null,"apc_paid":null,"fwci":4.2766,"has_fulltext":false,"cited_by_count":54,"citation_normalized_percentile":{"value":0.94484734,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"9","issue":"2","first_page":"5","last_page":"12"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10799","display_name":"Data Visualization and Analytics","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10799","display_name":"Data Visualization and Analytics","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9822999835014343,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9812999963760376,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.8129914999008179},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.791892409324646},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.6627305746078491},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.585067093372345},{"id":"https://openalex.org/keywords/clustering-high-dimensional-data","display_name":"Clustering high-dimensional data","score":0.5774555802345276},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5732620358467102},{"id":"https://openalex.org/keywords/linear-subspace","display_name":"Linear subspace","score":0.5115681290626526},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4671323597431183},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4465819299221039},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.43895500898361206},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.43880516290664673},{"id":"https://openalex.org/keywords/correlation-clustering","display_name":"Correlation clustering","score":0.430764377117157},{"id":"https://openalex.org/keywords/consensus-clustering","display_name":"Consensus clustering","score":0.42178744077682495},{"id":"https://openalex.org/keywords/cure-data-clustering-algorithm","display_name":"CURE data clustering algorithm","score":0.3741464614868164},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16817384958267212},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.10796275734901428}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.8129914999008179},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.791892409324646},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.6627305746078491},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.585067093372345},{"id":"https://openalex.org/C184509293","wikidata":"https://www.wikidata.org/wiki/Q5136711","display_name":"Clustering high-dimensional data","level":3,"score":0.5774555802345276},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5732620358467102},{"id":"https://openalex.org/C12362212","wikidata":"https://www.wikidata.org/wiki/Q728435","display_name":"Linear subspace","level":2,"score":0.5115681290626526},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4671323597431183},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4465819299221039},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.43895500898361206},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.43880516290664673},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.430764377117157},{"id":"https://openalex.org/C186767784","wikidata":"https://www.wikidata.org/wiki/Q5162841","display_name":"Consensus clustering","level":5,"score":0.42178744077682495},{"id":"https://openalex.org/C33704608","wikidata":"https://www.wikidata.org/wiki/Q5014717","display_name":"CURE data clustering algorithm","level":4,"score":0.3741464614868164},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16817384958267212},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.10796275734901428},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/1345448.1345451","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1345448.1345451","pdf_url":null,"source":{"id":"https://openalex.org/S4210176598","display_name":"ACM SIGKDD Explorations Newsletter","issn_l":"1931-0145","issn":["1931-0145","1931-0153"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM SIGKDD Explorations Newsletter","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W121350155","https://openalex.org/W190430544","https://openalex.org/W592308182","https://openalex.org/W1515984610","https://openalex.org/W1530453821","https://openalex.org/W1562753845","https://openalex.org/W1567056755","https://openalex.org/W1597063124","https://openalex.org/W1672197616","https://openalex.org/W1977496278","https://openalex.org/W2006533296","https://openalex.org/W2017238378","https://openalex.org/W2084812512","https://openalex.org/W2102880413","https://openalex.org/W2109130989","https://openalex.org/W2112791883","https://openalex.org/W2119111481","https://openalex.org/W2129905273","https://openalex.org/W2140190241","https://openalex.org/W2150926065","https://openalex.org/W2151530263","https://openalex.org/W2152825437","https://openalex.org/W2167091752","https://openalex.org/W2170581471","https://openalex.org/W2171036274","https://openalex.org/W2242960832","https://openalex.org/W2799061466","https://openalex.org/W3112020351","https://openalex.org/W4205687621","https://openalex.org/W4233014035","https://openalex.org/W6640101217","https://openalex.org/W6680704940","https://openalex.org/W7014191107"],"related_works":["https://openalex.org/W4301002638","https://openalex.org/W2606552659","https://openalex.org/W2111119584","https://openalex.org/W3186815950","https://openalex.org/W2111827030","https://openalex.org/W2311450085","https://openalex.org/W3035964814","https://openalex.org/W3124860551","https://openalex.org/W4232587025","https://openalex.org/W2309230723"],"abstract_inverted_index":{"To":[0,13],"gain":[1],"insight":[2],"into":[3,137],"today's":[4],"large":[5],"data":[6,8,33,40,163,199],"resources,":[7],"mining":[9,34],"extracts":[10],"interesting":[11],"patterns.":[12],"generate":[14],"knowledge":[15],"from":[16,20],"patterns":[17,27,42,54,62],"and":[18,94,111,140,164,200,207],"benefit":[19],"human":[21],"cognitive":[22],"abilities,":[23],"meaningful":[24],"visualization":[25,93,124,194],"of":[26,58,77,102,148,159,176],"are":[28,63,119],"crucial.":[29],"Clustering":[30],"is":[31],"a":[32,112],"technique":[35],"that":[36,126],"aims":[37],"at":[38],"grouping":[39],"to":[41,83,99,129,135,141,154,166,181],"based":[43],"on":[44,161,196],"mutual":[45],"(dis)similarity.":[46],"For":[47,74],"high":[48],"dimensional":[49],"data,":[50],"subspace":[51,57,78,87,109,117,133,143,183,189,211],"clustering":[52,88,110,170,184,212],"searches":[53],"in":[55,70],"any":[56],"the":[59,71,100,131,157,168,182,188],"attributes":[60,69],"as":[61],"typically":[64],"obscured":[65],"by":[66],"many":[67],"irrelevant":[68],"full":[72],"space.":[73],"visual":[75],"analysis":[76,178],"clusters,":[79],"their":[80,162],"comparability":[81],"has":[82],"be":[84],"ensured.":[85],"Existing":[86],"approaches,":[89],"however,":[90],"lack":[91],"interactive":[92],"show":[95],"bias":[96],"with":[97,209],"respect":[98],"dimensionality":[101,107],"subspaces.":[103],"In":[104],"this":[105],"work,":[106],"unbiased":[108],"novel":[113],"distance":[114],"function":[115],"for":[116,172,179],"clusters":[118],"proposed.":[120],"We":[121,191],"suggest":[122],"two":[123],"techniques":[125,195],"allow":[127],"users":[128,153],"browse":[130],"entire":[132],"clustering,":[134],"zoom":[136],"individual":[138],"objects,":[139],"analyze":[142],"cluster":[144],"characteristics":[145],"in-depth.":[146],"Bracketing":[147],"different":[149],"parameter":[150],"settings":[151],"enable":[152],"immediately":[155],"see":[156],"effect":[158],"parameters":[160],"hence":[165],"choose":[167],"best":[169],"result":[171],"further":[173],"analysis.":[174],"Usage":[175],"user":[177],"feedback":[180],"algorithm":[185],"directly":[186],"improves":[187],"clustering.":[190],"demonstrate":[192],"our":[193],"real":[197],"world":[198],"confirm":[201],"results":[202],"through":[203],"additional":[204],"accuracy":[205],"measurements":[206],"comparison":[208],"existing":[210],"algorithms.":[213]},"counts_by_year":[{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":2},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":5},{"year":2016,"cited_by_count":3},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":8},{"year":2013,"cited_by_count":2},{"year":2012,"cited_by_count":4}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2016-06-24T00:00:00"}